1. Field of the Invention
The present invention relates generally to an image processing method, an image processing apparatus, and a computer program product.
2. Description of the Related Art
Conventionally, as a method for extracting a skin region, a method that utilizes a model trained with color data about skin regions of a large number of people is generally used. For example of this method, see K. Sobottka and I. Pitas, “Face localization and facial feature extraction based on shape and color information”, Proceedings of the IEEE International Conference on Image Processing, Lausanne, Switzerland, September 1996, vol. 3, pp. 236-241 and/or A. Albiol, et al., “Optimum Color Spaces for Skin Detection”, Proceedings of the International Conference on Image Processing, 2001.
Each of these methods defines a certain range in a specific color space as a skin-color region, and segments an input image according to a model constructed from the skin-color region. However, various errors, i.e., variations in lighting, race, individual differences, and the like are undesirably incorporated in the constructed model and make it difficult to derive high accuracy from these methods.
Another approach makes adaptation by preparing a plurality of models in advance and appropriately changing a model according to an input (see, e.g., Q. Zhu, at el., “An adaptive skin model and its application to objectionable image filtering”, Proceedings of the 12th annual ACM international conference on Multimedia, pp. 56-63). This approach increases accuracy by performing two-step filtering: first, input data is classified using a generic skin-color model; and at a latter step, filtering using an adaptive model is performed. However, this approach performs processing based only on pixel color information and makes determination as to whether or not a skin-similar region is a true skin region based on the color information. Accordingly, even when a non-skin region contains a same color as skin, this region is undesirably utilized as sample data. In this case, an adaptive model, which is trained at the latter step, inevitably contains a noise; as a result, accuracy decreases.
Another approach identifies a skin region based on information other than color and performs adaptive processing using the information (see, e.g., F. Dadgostar and A. Sarrafzadeh, “An adaptive real-time skin detector based on Hue thresholding: A comparison on two motion tracking models”, Pattern Recognition Letters, 2006). According to this example, a region to be tracked is determined using a generic skin-color model. A local filter is created based on a result of motion detection in the region, and definitive region extraction is performed using the filter. This approach is effective in application to moving video images, in which a moving object is a human, for example; however, this approach is not applicable to still images or a general image sequence. Even when applied to video data, this approach cannot yield a sufficiently high accuracy because definitive region segmentation is performed only by adaptive Hue thresholding.
As described above, conventional techniques fail to robustly extract a specific region, such as a skin-color region, that varies greatly depending on lighting, differences among individuals, and the like.
Accordingly, there is a need to provide an image processing method and a computer program product for extracting a specific region from an input image with high accuracy.